Systems biology methods have shown great promise in providing a better understanding of human disease, and in identifying new disease targets. Nonetheless, it remains extraordinarily difficult to identify causal genes in most genetic diseases, in particular highly polygenic disorders, for which current approaches are most limited. These methods also typically leave off once the target is identified, and further research transitions to traditional paradigms for drug discovery. We hypothesize (the 'phenolog' hypothesis) that the identification of equivalent gene networks in humans and model organisms will reveal new candidate disease genes and new model systems for diseases. Moreover, such models can lead to the possibility of pursuing drug discovery based on the networks in the model organisms. We suggest that because pathways can evolve and be repurposed in different organisms that phenologs, similar (or orthologous) gene networks that nonetheless produce different phenotypes, may be present, and that these phenologs provide a basis not just for screening against a single protein, but rather for the identification of and simultaneous drug discovery efforts against multiple different targets in parallel. As an example of the importance of appreciating the evolutionary repurposing of pathways, we identify a yeast model of angiogenesis, and its subsequent application to disease gene and drug discovery. The same theoretical framework suggests a mouse model of autism, a worm model of breast cancer, and more. Our major aim is to test the phenolog hypothesis, primarily using the yeast model to discover new angiogenesis genes & performing yeast-based compound screening to find new classes of anti-angiogenesis inhibitors, suitable as lead compounds for anti-cancer therapies. Phenologs offer the possibility of associating new genes with polygenic diseases, as well as opening up drug screens in model organisms and follow-up studies searching for genetic variation in the candidate disease genes. The theoretical phenolog framework thus has the potential to impact a wide variety of diseases and could potentially affect a large downstream community.